Genealogy item ranking and recommendation

ABSTRACT

Systems and methods for training a machine learning (ML) ranking model to rank genealogy hints are described herein. One method includes retrieving a plurality of genealogy hints for a target person, where each of the plurality of genealogy hints corresponds to a genealogy item and has a hint type of a plurality of hint types. The method includes generating, for each of the plurality of genealogy hints, a feature vector having a plurality of feature values, the feature vector being included in a plurality of feature vectors. The method includes extending each of the plurality of feature vectors by at least one additional feature value based on the number of features of one or more other hint types of the plurality of hint types. The method includes training the ML ranking model using the extended plurality of feature vectors and user-provided labels.

CROSS-REFERENCES TO RELATED APPLICATIONS

The application is a continuation of U.S. patent application Ser. No.16/406,891 filed on May 8, 2019, which claims priority to U.S.Provisional Patent Application No. 62/668,269, filed May 8, 2018,entitled “LEARNING TO RANK FOR GENEALOGY RESOURCE RECOMMENDATION,” andto U.S. Provisional Patent Application No. 62/668,795, filed May 8,2018, entitled “LEARNING TO RANK FOR GENEALOGY RESOURCE RECOMMENDATION,”the entire content of each of which is herein incorporated in itsentirety.

BACKGROUND OF THE INVENTION

In certain genealogical or family history databases, ancestor data isstored in trees which contain one or more persons or individuals. Treesmay also include intra-tree relationships which indicate therelationships between the various individuals within a certain tree. Inmany cases, persons in one tree may correspond to persons in othertrees, as users have common ancestors with other users. One challenge inmaintaining genealogical databases has been entity resolution, whichrefers to the problem of identifying and linking differentmanifestations of the same real-world entity. For example, manymanifestations of the same person may appear across multiple trees. Thisproblem arises due to discrepancies between different historicalrecords, discrepancies between historical records and human accounts,and discrepancies between different human accounts. For example,different users having a common ancestor may have different opinions asto the name, date of birth, and place of birth of that ancestor. Theproblem becomes particularly prevalent when large amounts of historicaldocuments are difficult to read, causing a wide range of possibleancestor data.

Another challenge in maintaining genealogical databases relates toproviding a robust recommender system with an efficient rankingalgorithm to help genealogy enthusiasts find relevant information oftheir ancestors so as to better discover their family history. Whileranking strategies have been applied to recommend and rank items in manyapplications, no efficient methodology to rank ancestry items currentlyexists. Accordingly, there is a need for improved techniques in thearea.

BRIEF SUMMARY OF THE INVENTION

Examples given below provide a summary of the present invention. As usedbelow, any reference to a series of examples is to be understood as areference to each of those examples disjunctively (e.g., “Examples 1-4”is to be understood as “Examples 1, 2, 3, or 4”).

Example 1 is a method of training a machine learning (ML) ranking modelto rank genealogy hints, the method comprising: retrieving a pluralityof genealogy hints for a target person, wherein each of the plurality ofgenealogy hints corresponds to a genealogy item and has a hint type of aplurality of hint types, wherein each of the plurality of hint types hasa number of features; generating, for each of the plurality of genealogyhints, a feature vector having a plurality of feature values, thefeature vector being included in a plurality of feature vectors;extending each of the plurality of feature vectors by at least oneadditional feature value based on the number of features of one or moreother hint types of the plurality of hint types; creating a firsttraining set based on the plurality of feature vectors; training the MLranking model in a first stage using the first training set; creating asecond training set including a subset of the plurality of genealogyhints that were incorrectly ranked after the first stage; and trainingthe ML ranking model in a second stage using the second training set.

Example 2 is the method of example(s) 1, wherein the ML ranking model isa neural network.

Example 3 is the method of example(s) 1-2, wherein the plurality of hinttypes includes one or more of: a record hint type; a photo hint type; ora story hint type.

Example 4 is the method of example(s) 1-3, wherein the number of theplurality of feature values in the feature vector generated for each ofthe plurality of genealogy hints is equal to the number of features forthe hint type.

Example 5 is the method of example(s) 1-4, wherein each of the pluralityof feature vectors are extended through zero padding.

Example 6 is the method of example(s) 1-5, further comprising: receivinga user input indicating the target person.

Example 7 is the method of example(s) 1-6, further comprising: receivinga user input providing a ranking label, wherein the second training setis created based on the ranking label.

Example 8 is a non-transitory computer-readable medium comprisinginstructions that, when executed by one or more processors, cause theone or more processors to perform operations comprising: retrieving aplurality of genealogy hints for a target person, wherein each of theplurality of genealogy hints corresponds to a genealogy item and has ahint type of a plurality of hint types, wherein each of the plurality ofhint types has a number of features; generating, for each of theplurality of genealogy hints, a feature vector having a plurality offeature values, the feature vector being included in a plurality offeature vectors; extending each of the plurality of feature vectors byat least one additional feature value based on the number of features ofone or more other hint types of the plurality of hint types; creating afirst training set based on the plurality of feature vectors; training amachine learning (ML) ranking model in a first stage using the firsttraining set; creating a second training set including a subset of theplurality of genealogy hints that were incorrectly ranked after thefirst stage; and training the ML ranking model in a second stage usingthe second training set.

Example 9 is the non-transitory computer-readable medium of example(s)8, wherein the ML ranking model is a neural network.

Example 10 is the non-transitory computer-readable medium of example(s)8-9, wherein the plurality of hint types includes one or more of: arecord hint type; a photo hint type; or a story hint type.

Example 11 is the non-transitory computer-readable medium of example(s)8-10, wherein the number of the plurality of feature values in thefeature vector generated for each of the plurality of genealogy hints isequal to the number of features for the hint type.

Example 12 is the non-transitory computer-readable medium of example(s)8-11, wherein each of the plurality of feature vectors are extendedthrough zero padding.

Example 13 is the non-transitory computer-readable medium of example(s)8-12, wherein the operations further comprise: receiving a user inputindicating the target person.

Example 14 is the non-transitory computer-readable medium of example(s)8-13, wherein the operations further comprise: receiving a user inputproviding a ranking label, wherein the second training set is createdbased on the ranking label.

Example 15 is a system comprising: one or more processors; and anon-transitory computer-readable medium comprising instructions that,when executed by the one or more processors, cause the one or moreprocessors to perform operations comprising: retrieving a plurality ofgenealogy hints for a target person, wherein each of the plurality ofgenealogy hints corresponds to a genealogy item and has a hint type of aplurality of hint types, wherein each of the plurality of hint types hasa number of features; generating, for each of the plurality of genealogyhints, a feature vector having a plurality of feature values, thefeature vector being included in a plurality of feature vectors;extending each of the plurality of feature vectors by at least oneadditional feature value based on the number of features of one or moreother hint types of the plurality of hint types; creating a firsttraining set based on the plurality of feature vectors; training amachine learning (ML) ranking model in a first stage using the firsttraining set; creating a second training set including a subset of theplurality of genealogy hints that were incorrectly ranked after thefirst stage; and training the ML ranking model in a second stage usingthe second training set.

Example 16 is the system of example(s) 15, wherein the ML ranking modelis a neural network.

Example 17 is the system of example(s) 15-16, wherein the plurality ofhint types includes one or more of: a record hint type; a photo hinttype; or a story hint type.

Example 18 is the system of example(s) 15-17, wherein the number of theplurality of feature values in the feature vector generated for each ofthe plurality of genealogy hints is equal to the number of features forthe hint type.

Example 19 is the system of example(s) 15-18, wherein each of theplurality of feature vectors are extended through zero padding.

Example 20 is the system of example(s) 15-19, wherein the operationsfurther comprise: receiving a user input indicating the target person.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the invention, are incorporated in and constitute apart of this specification, illustrate embodiments of the invention andtogether with the detailed description serve to explain the principlesof the invention. No attempt is made to show structural details of theinvention in more detail than may be necessary for a fundamentalunderstanding of the invention and various ways in which it may bepracticed.

FIG. 1 illustrates various trees having similar individuals, accordingto an embodiment of the present disclosure.

FIG. 2 illustrates a filtering step to retrieve genealogy items for atarget person, according to some embodiments of the present invention.

FIG. 3 illustrates a genealogy item ranking system, according to someembodiments of the present invention.

FIG. 4 illustrates a method of training one or more feature generatorsof a genealogy item ranking system, according to some embodiments of thepresent invention.

FIG. 5 illustrates a method of training a machine learning rankingmodel, according to some embodiments of the present invention.

FIG. 6 illustrates an example of generating feature vectors for tworecords.

FIG. 7 illustrates an example of generating feature vectors for twophotos.

FIG. 8 illustrates an example of generating feature vectors for twostories.

FIG. 9 illustrates an example of generating extended feature vectorsfrom feature vectors.

FIG. 10 illustrates a method of training a machine learning rankingmodel, according to some embodiments of the present invention.

FIG. 11 shows a simplified computer system, according to someembodiments of the present invention.

In the appended figures, similar components and/or features may have thesame numerical reference label. Further, various components of the sametype may be distinguished by following the reference label with a letteror by following the reference label with a dash followed by a secondnumerical reference label that distinguishes among the similarcomponents and/or features. If only the first numerical reference labelis used in the specification, the description is applicable to any oneof the similar components and/or features having the same firstnumerical reference label irrespective of the suffix.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the present invention provide for systems, methods, andother techniques for ranking genealogy items for a user. Techniquesdescribed herein allow various types of information to be ranked,including family history records, photos, and stories. Types of familyhistory records can include birth records, marriage records, immigrationrecords, etc. As there exists huge amounts of genealogy item of varioustypes, recommending relevant information and prioritizing items in apreferred order is helpful for genealogy enthusiasts in the journey ofdiscovering their family history, since a robust recommender system andan efficient ranking algorithm could greatly save time while improvinguser experience.

To effectively recommend the best genealogy items from billions ofpotential items, the scope of the data is limited via entity resolution,which includes identifying and linking different manifestations of thesame real-world entity. The data can consist of many genealogical treesand records. The trees often have nodes that overlap with each other,creating duplicate entities. Additionally, various records can existthat refer to the same entity while varying in their content. Forexample, a birth record for an individual includes birth informationwhile a marriage record includes marriage information. Furthermore,since the tree data can be user generated and the records are mostlykeyed from historical records, typos and errors are possible that canadd noise to the data.

In order to resolve which records and tree nodes refer to whichreal-world entities, the problem is reduced to a pairwise classificationproblem where the classes are “Match” or “Non-Match”. There are manycriteria to consider when establishing what constitutes a match.Previously studied methodologies include techniques such as exact match,distance match, and TF/IDF matching for text data. A novel approach isemployed herein that attempts to replicate the matching criteria of agenealogist and uses a machine learning (ML) algorithm to combine allthese criteria into a prediction score. The approach employs ahierarchical classification algorithm which leverages the familialstructure of genealogical records. The model trains on thousands ofpairwise entity comparisons labeled by professional genealogists todetermine whether two records or tree nodes refer to the same entity.This approach provides significant improvements over previous rule-basedapproaches used for entity recognition. The data also includes contentattached by users such as photos and stories. The attached content isalso associated with the resolved entity and thus becomes available as arelevant genealogy resource for that entity.

Once the relevant records are associated together with photos andstories, a ranking methodology is employed to appropriately rank themfor users. Each item in the recommendation list is referred to as a“hint”. Each hint may be labeled by users with one of three actions:accept, reject, or maybe. This may be the relevance score of each item,constituting labels in the training data that the ML ranking model canmake use of

Given the set of thousands of labeled compares from professionalgenealogists, the information a genealogist would use is encoded in amanner suitable for a ML algorithm. For example, information on namesimilarity, name uniqueness, and historically common migration patterns.In addition, for some compares, information on other members of thefamily such as spouse, mother, and children is used. This allows theextent to which the family structure matches to be analyzed. In someembodiments of the present invention, the mother, father, spouse, andthree children are considered for each compare.

For each family member comparison, around 50 features from each pair areextracted (for example, mothers are compared to mothers, fathers tofathers, etc.). These features include information on names, births,deaths, and other information from the compare. In addition to thesefeatures, family level features are extracted which encompass data fromall members of the family. For example, the extent to which thelocations and times of events match across both families is analyzed.

The entity resolution algorithm implements a two-step learning process.First, the model is trained using only features from the person ofinterest—excluding family relations. Once trained, this model allows thestrength of comparisons between family members to be evaluated. Forexample, given a comparison between two persons, this model can be usedto determine how well their mothers match. Once this model has extractedthe most similar relations for mothers, fathers, spouses, and 3children, the features from all these relations can be extracted. Thisresults in close to 400 features including the actual probabilitiesreturned from the first model on the closeness of the family relations.Another model is then trained using this extended feature set. Thismodel can be referred to as the family model because it leveragesinformation from the family relations of the entities.

The entity resolution algorithm described above is used to identifyrelevant genealogy items for the ML ranking algorithm. One goal of theML ranking algorithm is to provide an optimal ranking for a list ofitems based on the descending order of the item score. The item scoringfunction can be defined on weights that indicate the contribution offeatures to the ranking function. Labels are the ground truth relevancescores of items. Label quality is important to ranking performance, asoptimal weights of features in the ranking function are learned fromtraining data so that the ranking function generates high scores foritems with high relevance scores.

Relevance scores can be obtained from explicit or implicit feedback fromusers. Explicit feedback requires cognitive effort to collect people'sdirect feedback. The alternative is implicit feedback which is abundantand easy to collect. Implicit feedback is domain dependent, varying fromplay times of a track in music recommendation system, time spent readinga page in web search, and click times of a product in E-Commerce search.The challenge when using implicit feedback is how to use it in areasonable way. Implicit feedback is incorporated herein by each hintbeing given one of three actions by genealogy enthusiasts: accept,reject, or maybe. The explicit feedback generates three different levelsof relevance scores for each hint. Some embodiments of the presentinvention use 3, 2, and 1 to indicate accepted, maybe, and rejectedhints respectively. This generates labels in the training data.

Features are defined on each of the pairs between a target person and ahint. First, available information from a target person is extracted,such as the first name, last name, birth place, etc. Next, features aredefined between the target person and each hint from each hint type,including record hints, photo hints, and story hints. Two differenttypes of features are defined: record-specific features and relevancefeatures. Information from records are extracted to calculaterecord-specific features by determining whether certain fields exist inboth the target person and the hint.

To facilitate the ranking of digitized genealogy photos, image featureextraction is accomplished using deep learning convolutional neuralnetworks to classify a photo into a unique category. By using categoriesas features for photos, certain categories can be found to be morevaluable than others, and the ML ranking model can learn the weightsassociated with each category. In one implementation, GoogleNet wasselected as the network architecture, and the model was trained usingapproximately 50,000 labeled training images for 30 epochs. The modeltraining plateaued at 97.8% accuracy after the 20th epoch. All imageswere converted to color 256×256 format, and mean image subtraction wasused. The model was trained to recognize 70 classes of content comprisedof images containing photos, documents, headstones, coat-of-arms, flags,etc.

Story hints are valuable because personal stories one user contributesand uploads could be richly rewarding information for others. Featurevectors are generated for stories by calculating the similaritiesbetween the stories and the corresponding target persons. Specifically,the facts regarding a target person (e.g., name, birth, death, etc.) arecompared to keywords extracted from stories. Then a string similaritymetric (e.g., Jaro-Winkler distance) may be used for measuring thedistance between the two sequences. Feature vectors for story hints mayalso be generated using a neural network, which may be trained usinguser-provided labels, as described herein. Accordingly, in someembodiments, item-specific features are defined for records and photosand relevance-based features are defined for records and stories.

FIG. 1 illustrates three separate trees 100 a-c, each containing asimilar individual 102 a-c, respectively. Trees 100 a-c are also denotedas Trees A, B, and C, respectively. Trees A, B, and C may be owned by,created by, and/or used by Tree Persons A1, B1, and C1, or by otherusers unrelated to persons in Trees A, B, and C. In some embodiments, itmay be determined that Tree Person A15 (named “John Doe”), Tree PersonB13 (named “Jonathan Doe”), and Tree Person C5 (named “Johnny Doe”)correspond to the same real-life individual based on their similarity.Although a user of Tree A may understand Tree Person A15 to be John Doe,it may be beneficial to that user to become aware of the informationdiscovered by the users of Trees B and C, who understand John Doe tohave a differently spelled name and a different date of death.Similarly, users of Trees B and C may benefit to know of alternatespellings and dates of death for Tree Persons B13 and C5, whom theyunderstand to be Jonathan Doe and Johnny Doe. Therefore, to assist usersof Trees A, B, and C in their genealogical research, it is oftenadvantageous to identify, group, and possibly merge together treepersons that are determined to correspond to the same real-lifeindividual.

One method for determining whether Tree Persons A15, B13, and C5correspond to the same real-life individual is a rule-based algorithm inwhich a human expert looks at different pairs of persons and createsrules. For example, consider that two persons are named “Jack Smith” butone is born in Mar. 1, 1981 and the other is born in Mar. 1, 1932. Arule-based algorithm may generate four separate scores, one for acomparison of the names (a high score in this example), one for acomparison of the month of birth (a high score in this example), one fora comparison of the day of birth (a high score in this example), and onefor the year of birth (a low score in this example). The four separatescores are added together to generate a final similarity score. Thehigher the similarity score, the higher the probability that the twotree persons correspond to the same real-life individual.

There are several disadvantages to rule-based algorithms. First, theyare subjective. When scores are combined into a final similarity score,they may be weighted such that the final similarity score is overlysensitive to the chosen weighting, which may be arbitrary. Second,rule-based algorithms become extremely complicated as they must accountfor several special cases, such as popular names. Third, rule-basedalgorithms are difficult to update and maintain. Over time, there may behundreds of rules to generate a single final similarity score. If newspecial cases arise, a human expert has to verify whether all thepreviously generated rules will apply to the new case or not. If aparticular rule does not apply, then a change may be needed.

Accordingly, in some embodiments, a ML model is used to perform entityresolution to determine that Tree Persons A15, B13, and C5 correspond tothe same real-life individual.

FIG. 2 illustrates a filtering step to retrieve genealogy items for atarget person, according to some embodiments of the present invention.An entity or target person 206 is selected form a set of entities ortarget persons. Tree database 202 is scanned to retrieve treescontaining the target person as well as the genealogy items associatedwith the target person. In the illustrated example, 5 trees areretrieved from tree database 202 that contain target person 206. Foreach of the trees, genealogy items 208 associated with the target personare also retrieved. Genealogy items 208 may have various types,including records 210, photos 212, stories 214, and other items 216.

FIG. 3 illustrates a genealogy item ranking system 300, according tosome embodiments of the present invention. In some embodiments,genealogy item ranking system 300 receives hints 302 of four hint types:record hints, photo hints, story hints, and other hints. For example,hints 302 may include record hints 304, photo hints 306, story hints308, and other hints 310. Each of hints 302 may correspond to agenealogy item stored in a database. For example, each of record hints304 may correspond to a record, each of photo hints 306 may correspondto a photo, and each of story hints 308 may correspond to a story. Insome embodiments, each of other hints 310 may correspond to relatedaudio or video information.

In some embodiments, genealogy item ranking system 300 includes a recordfeature generator 312 that generates a feature vector f_(1-J) for eachof record hints 304. Feature vector f_(1-J) may include J featurevalues, where J is the number of features for the record hint type. Insome embodiments, each feature value of feature vector f_(1-J) indicateswhether a particular feature is found in the corresponding record (e.g.,birth date, marriage date, etc.). In some embodiments, record featuregenerator 312 comprises a ML model, such as a neural network.

In some embodiments, genealogy item ranking system 300 includes a photofeature generator 314 that generates a feature vector f_(1-K) for eachof photo hints 306. Feature vector f_(1-K) may include K feature values,where K is the number of features for the photo hint type. In someembodiments, each feature value of feature vector f_(1-K) indicateswhether a particular feature is found in the corresponding photo (e.g.,people, landscape, etc.). In some embodiments, photo feature generator314 comprises a ML model, such as a neural network.

In some embodiments, genealogy item ranking system 300 includes a storyfeature generator 316 that generates a feature vector f_(1-L) for eachof story hints 308. Feature vector f_(1-L) may include L feature values,where L is the number of features for the story hint type. In someembodiments, each feature value of feature vector f_(1-L) indicateswhether a particular feature is found in the corresponding story (e.g.,name, birth date, etc.). In some embodiments, story feature generator316 comprises a ML model, such as a neural network.

In some embodiments, genealogy item ranking system 300 includes anotherfeature generator 318 that generates a feature vector f_(1-M) for eachof other hints 310. Feature vector f_(1-M) may include M feature values,where M is the number of features for the other hint type. In someembodiments, each feature value of feature vector f_(1-M) indicateswhether a particular feature is found in the corresponding other item.In some embodiments, other feature generator 318 comprises a ML model,such as a neural network.

In some embodiments, feature vectors 322 are extended by a featureextender 324, thereby generating extended feature vectors 326. In someembodiments, feature extender 324 adds at least one additional featurevalue to each of feature vectors 322. In some embodiments, all extendedfeature vectors 326 are normalized to have the same length (i.e., samenumber of feature values). In some embodiments, all extended featurevectors 326 are normalized to have the same value range (e.g., between 0and 1) for all feature values. The number of feature values that isadded to a particular feature vector is based on the hint type of theparticular feature vector. Specifically, the number of added features isthe cumulative number of feature values of the other hint types. Severalexamples are described below.

For a particular feature vector of feature vectors 322 that correspondsto one of record hints 304, the number of feature values in theparticular feature vector is J. The number of feature values that areadded to the particular feature vector is the sum of the number offeature values for the other three hint types: K for photo hints 306, Lfor story hints 308, and M for other hints 310. Accordingly, K+L+Mfeature values are added (e.g., appended to the beginning and/or end) tothe particular feature vector by feature extender 324 to generate anextended feature vector having J+K+L+M feature values.

Similarly, for a particular feature vector of feature vectors 322 thatcorresponds to one of photo hints 306, the number of feature values inthe particular feature vector is K. The number of feature values thatare added to the particular feature vector is the sum of the number offeature values for the other three hint types: J for record hints 304, Lfor story hints 308, and M for other hints 310. Accordingly, J+L+Mfeature values are added (e.g., appended to the beginning and/or end) tothe particular feature vector by feature extender 324 to generate anextended feature vector having J+K+L+M feature values.

Similarly, for a particular feature vector of feature vectors 322 thatcorresponds to one of story hints 308, the number of feature values inthe particular feature vector is L. The number of feature values thatare added to the particular feature vector is the sum of the number offeature values for the other three hint types: J for record hints 304, Kfor photo hints 306, and M for other hints 310. Accordingly, J+K+Mfeature values are added (e.g., appended to the beginning and/or end) tothe particular feature vector by feature extender 324 to generate anextended feature vector having J+K 30 L+M feature values.

Similarly, for a particular feature vector of feature vectors 322 thatcorresponds to one of other hints 310, the number of feature values inthe particular feature vector is M. The number of feature values thatare added to the particular feature vector is the sum of the number offeature values for the other three hint types: J for record hints 304, Kfor photo hints 306, and L for story hints 308. Accordingly, J+K+Lfeature values are added (e.g., appended to the beginning and/or end) tothe particular feature vector by feature extender 324 to generate anextended feature vector having J+K+L+M feature values.

In some embodiments, genealogy item ranking system 300 includes a MLranking model 330 for ranking hints 302 based on their correspondingextended feature vectors 326. In some embodiments, ML ranking model 330receives N extended feature vectors 326 as input and outputs a ranking(e.g., 1 through N) corresponding to the N extended feature vectors 326and the corresponding N hints 302. Alternatively, ML ranking model 330may be configured to only output a subset of the N hints, such as thetop 5 or top 10 ranked hints.

FIG. 4 illustrates a method of training one or more feature generatorsof a genealogy item ranking system, such as genealogy item rankingsystem 300, according to some embodiments of the present invention. Oneor more of feature generators 312, 314, 316, 318 may be trained usinguser-provided labels as follows.

When record feature generator 312 is implemented as a ML model, such asa neural network, it may be trained by inputting a record hint 304 torecord feature generator 312, which outputs feature vector f_(1-L). Auser then examines the record hint to create a record label l_(1-J). Forexample, the user may examine the corresponding record and determinewhether each particular feature is found in the record. The user mayenter the created label through a computer interface. An error vectore_(1-J) may be calculated as the difference between feature vectorf_(1-J) and record label l_(1-J). The ML model is then modified by amodifier 402 based on error vector e_(1-J). Modifier 402 may changeweights associated with record feature generator 312 such that featurevector f_(1-J) better approximates record label l_(1-J), causing errorvector e_(1-J) to be reduced. This process is repeated for multiplerecord hints 304 to train record feature generator 312.

Similarly, when photo feature generator 314 is implemented as a MLmodel, such as a neural network, it may be trained by inputting a photohint 306 to photo feature generator 314, which outputs feature vectorf_(1-K). A user then examines the photo hint to create a photo labell_(1-K). For example, the user may examine the corresponding photo anddetermine whether each particular feature is found in the photo. Theuser may enter the created label through a computer interface. An errorvector e_(1-K) may be calculated as the difference between featurevector f_(1-K) and record label l_(1-K). The ML model is then modifiedby a modifier 404 based on error vector e_(1-K). Modifier 404 may changeweights associated with photo feature generator 314 such that featurevector f_(1-K) better approximates record label l_(1-K) , causing errorvector e_(1-K) to be reduced. This process is repeated for multiplephoto hints 306 to train photo feature generator 314.

Similarly, when story feature generator 316 is implemented as a MLmodel, such as a neural network, it may be trained by inputting a storyhint 308 to story feature generator 316, which outputs feature vectorf_(1-L). A user then examines the story hint to create a story labell_(1-L). For example, the user may examine the corresponding story anddetermine whether each particular feature is found in the story. Theuser may enter the created label through a computer interface. An errorvector e_(1-L) may be calculated as the difference between featurevector f_(1-L) and record label l_(1-L). The ML model is then modifiedby a modifier 406 based on error vector e_(1-L). Modifier 406 may changeweights associated with story feature generator 316 such that featurevector f_(1-L) better approximates record label l_(1-L), causing errorvector e_(1-L)to be reduced. This process is repeated for multiple storyhints 308 to train story feature generator 316.

Similarly, when other feature generator 318 is implemented as a MLmodel, such as a neural network, it may be trained by inputting anotherhint 310 to other feature generator 318, which outputs feature vectorf_(1-M). A user then examines the other hint to create another labell_(1-M). The user may enter the created label through a computerinterface. An error vector e_(1-M) may be calculated as the differencebetween feature vector f_(1-M) and record label l_(1-M). The ML model isthen modified by a modifier 408 based on error vector e_(1-M). Modifier408 may change weights associated with other feature generator 318 suchthat feature vector f_(1-M) better approximates record label l_(1-M),causing error vector e_(1-M)to be reduced. This process is repeated formultiple other hints 310 to train other feature generator 318.

FIG. 5 illustrates a method of training ML ranking model 330, accordingto some embodiments of the present invention. ML ranking model 330 maybe trained after feature generators 312, 314, 316, 318 are trained byinputting one or more of extended feature vectors 326 to ML rankingmodel 330, which outputs hint ranking 332. A user may examine hintranking 332 and/or the corresponding hints 302 to create a ranking label502. Ranking label 502 may provide a complete ranking of the N hints302, may indicate which hints should be ranked higher or lower, or mayindicate which hints the user is not interested in, among otherpossibilities. A ranking error 504 may be generated based on thedifference between ranking label 502 and hint ranking 332.

ML ranking model 330 is then modified by a modifier 506 based on rankingerror 504. Modifier 506 may change weights associated with ML rankingmodel 330 such that hint ranking 332 better approximates ranking label502, causing ranking error 504 to be reduced. ML ranking model 330 canbe trained using different selections of extended feature vectors 326.For example, N extended feature vectors 326 may be randomly selected foreach training step. As the accuracy of ML ranking model 330 improves, Nmay be increased so that the likelihood of more similar extended featurevectors 326 being selected also increases.

FIG. 6 illustrates an example of generating feature vectors f_(1-J) fortwo records (“Record 1” and “Record 2”). The records are analyzed(either by a user or by record feature generator 312) for the presenceof the different features shown in column 602. In the illustratedexample, feature values only indicate the presence of the feature in therecord (1 if present and 0 if missing), and not the similarity betweenthe feature and the target person. For example, even though Record 1includes the misspelled first name “John” instead of the true spelling“Jon”, the feature value is set to 1 since that particular feature ispresent in Record 1. In some embodiments, the feature value may furtherindicate a similarity between the feature and the target person (e.g.,“John” may correspond to a feature value of 0.8 and “Jon” may correspondto a feature value of 1).

FIG. 7 illustrates an example of generating feature vectors f_(1-K) fortwo photos (“Photo 1” and “Photo 2”). The photos are analyzed (either bya user or by photo feature generator 314) for the presence (or for someother aspect) of the different features shown in column 702. In theillustrated example, feature values either indicate the presence of thefeature in the photo (e.g., color, landscape, writing) or aclassification with regards to a category (e.g., number of people,category). For example, Photo 1 includes color, a single person, nolandscape, writing, and is assigned to category 42, which may be acategory for immigration documents. As another example, Photo 2 includesno color, 7 people, no landscape, no writing, and is assigned tocategory 67, which may be a category for images of groups of people. Insome embodiments, the feature value may indicate a confidence in aclassification. For example, a photo with lots of color may correspondto a feature value of 1 and a photo with little color may correspond toa feature value of 0.3.

FIG. 8 illustrates an example of generating feature vectors f_(1-L) fortwo stories (“Story 1” and “Story 2”). The stories are analyzed (eitherby a user or by story feature generator 316) for the presence (or forsome other aspect) of the different features shown in column 802. In theillustrated example, the first feature indicates the similarity betweenthe story and the target person (e.g., similar names, locations, etc.),the second feature indicates the voice in which the story is written(e.g., first person, third person, etc.), and the third featureindicates the length of the story. For example, as shown by the featurevalues, although Story 1 is longer than Story 2, Story 1 has lesssimilarity to the target person than Story 2.

FIG. 9 illustrates an example of generating extended feature vectors 926from the feature vectors described in reference to FIGS. 6-8 . In theillustrated example, the extended feature vectors are generated byextending the feature vectors through zero padding to one or both endsof the feature vectors. For example, extended feature vectors 926-1,926-2 are generated by adding 8 feature values to the ends of the recordfeature vectors described in reference to FIG. 6 , extended featurevectors 926-3, 926-4 are generated by adding 7 feature values to thebeginnings and 3 feature values to the ends of the photo feature vectorsdescribed in reference to FIG. 7 , and extended feature vectors 926-5,926-6 are generated by adding 12 feature values to the beginnings of thestory feature vectors described in reference to FIG. 8 .

FIG. 10 illustrates a method 1000 of training a ML ranking model, suchas ML ranking model 330, according to some embodiments of the presentinvention. One or more steps of method 1000 may be performed in an orderdifferent than that shown in FIG. 10 , and one or more steps of method1000 may be omitted during performance of method 1000. In someembodiments, the ML ranking model is a neural network.

At step 1002, a plurality of genealogy hints for a target person areretrieved. Each of the plurality of genealogy hints may have a hint typeand may correspond to a genealogy item, such as a record, photo, orstory. Each of a plurality of hint types may have a predetermined numberof features.

At step 1004, a feature vector is generated for each of the plurality ofgenealogy hints. Each of the feature vectors may have a plurality offeature values. The feature vectors may collectively be referred to as aplurality of feature vectors.

At step 1006, each of the plurality of feature vectors are extended byat least one additional feature value based on the number of features ofthe other hint types of the plurality of hint types.

At step 1008, the ML ranking model is trained based on the extendedfeature vectors. In some embodiments, the ML ranking model is alsotrained based on user-provided labels. The ML ranking model may rank oneor more genealogy hints based on the extended feature vectors, and theranked hints may be compared to user-provided labels to generated anerror. The ML ranking model is then modified based on the error so thatthe error is reduced in subsequent iterations of training.

In some embodiments, the ML ranking model is trained over two stagesduring each training iteration. During a first stage, a first trainingset is created based on the plurality of extended feature vectors. Forexample, the first training set may include the plurality of extendedfeature vectors. Further during the first stage, the ML ranking model isprovided with the plurality of extended feature vectors so as togenerate ranked hints. During a second stage, a second training set iscreated including one or more of the ranked hints that were rankedincorrectly. The incorrectly ranked hints are obtained by comparing theranked hints to user-provided labels and determining differences betweenthe two. Further during the second stage, the incorrectly ranked hintsmay be used to modify (i.e., train) the ML ranking model so that theerror is reduced in subsequent iterations of training.

FIG. 11 shows a simplified computer system 1100, according to someembodiments of the present invention. FIG. 11 provides a schematicillustration of one embodiment of a computer system 1100 that canperform some or all of the steps of the methods provided by variousembodiments. It should be noted that FIG. 11 is meant only to provide ageneralized illustration of various components, any or all of which maybe utilized as appropriate. FIG. 11 , therefore, broadly illustrates howindividual system elements may be implemented in a relatively separatedor relatively more integrated manner.

The computer system 1100 is shown comprising hardware elements that canbe electrically coupled via a bus 1105, or may otherwise be incommunication, as appropriate. The hardware elements may include one ormore processors 1110, including without limitation one or moregeneral-purpose processors and/or one or more special-purpose processorssuch as digital signal processing chips, graphics accelerationprocessors, and/or the like; one or more input devices 1115, which caninclude without limitation a mouse, a keyboard, a camera, and/or thelike; and one or more output devices 1120, which can include withoutlimitation a display device, a printer, and/or the like.

The computer system 1100 may further include and/or be in communicationwith one or more non-transitory storage devices 1125, which cancomprise, without limitation, local and/or network accessible storage,and/or can include, without limitation, a disk drive, a drive array, anoptical storage device, a solid-state storage device, such as a randomaccess memory (“RAM”), and/or a read-only memory (“ROM”), which can beprogrammable, flash-updateable, and/or the like. Such storage devicesmay be configured to implement any appropriate data stores, includingwithout limitation, various file systems, database structures, and/orthe like.

The computer system 1100 might also include a communications subsystem1130, which can include without limitation a modem, a network card(wireless or wired), an infrared communication device, a wirelesscommunication device, and/or a chipset such as a Bluetooth™ device, an802.11 device, a WiFi device, a WiMax device, cellular communicationfacilities, etc., and/or the like. The communications subsystem 1130 mayinclude one or more input and/or output communication interfaces topermit data to be exchanged with a network such as the network describedbelow to name one example, other computer systems, television, and/orany other devices described herein. Depending on the desiredfunctionality and/or other implementation concerns, a portableelectronic device or similar device may communicate image and/or otherinformation via the communications subsystem 1130. In other embodiments,a portable electronic device, e.g. the first electronic device, may beincorporated into the computer system 1100, e.g., an electronic deviceas an input device 1115. In some embodiments, the computer system 1100will further comprise a working memory 1135, which can include a RAM orROM device, as described above.

The computer system 1100 also can include software elements, shown asbeing currently located within the working memory 1135, including anoperating system 1140, device drivers, executable libraries, and/orother code, such as one or more application programs 1145, which maycomprise computer programs provided by various embodiments, and/or maybe designed to implement methods, and/or configure systems, provided byother embodiments, as described herein. Merely by way of example, one ormore procedures described with respect to the methods discussed above,such as those described in relation to FIG. 11 , might be implemented ascode and/or instructions executable by a computer and/or a processorwithin a computer; in an aspect, then, such code and/or instructions canbe used to configure and/or adapt a general purpose computer or otherdevice to perform one or more operations in accordance with thedescribed methods.

A set of these instructions and/or code may be stored on anon-transitory computer-readable storage medium, such as the storagedevice(s) 1125 described above. In some cases, the storage medium mightbe incorporated within a computer system, such as computer system 1100.In other embodiments, the storage medium might be separate from acomputer system e.g., a removable medium, such as a compact disc, and/orprovided in an installation package, such that the storage medium can beused to program, configure, and/or adapt a general purpose computer withthe instructions/code stored thereon. These instructions might take theform of executable code, which is executable by the computer system 1100and/or might take the form of source and/or installable code, which,upon compilation and/or installation on the computer system 1100 e.g.,using any of a variety of generally available compilers, installationprograms, compression/decompression utilities, etc., then takes the formof executable code.

It will be apparent to those skilled in the art that substantialvariations may be made in accordance with specific requirements. Forexample, customized hardware might also be used, and/or particularelements might be implemented in hardware, software including portablesoftware, such as applets, etc., or both. Further, connection to othercomputing devices such as network input/output devices may be employed.

As mentioned above, in one aspect, some embodiments may employ acomputer system such as the computer system 1100 to perform methods inaccordance with various embodiments of the technology. According to aset of embodiments, some or all of the procedures of such methods areperformed by the computer system 1100 in response to processor 1110executing one or more sequences of one or more instructions, which mightbe incorporated into the operating system 1140 and/or other code, suchas an application program 1145, contained in the working memory 1135.Such instructions may be read into the working memory 1135 from anothercomputer-readable medium, such as one or more of the storage device(s)1125. Merely by way of example, execution of the sequences ofinstructions contained in the working memory 1135 might cause theprocessor(s) 1110 to perform one or more procedures of the methodsdescribed herein. Additionally or alternatively, portions of the methodsdescribed herein may be executed through specialized hardware.

The terms “machine-readable medium” and “computer-readable medium,” asused herein, refer to any medium that participates in providing datathat causes a machine to operate in a specific fashion. In an embodimentimplemented using the computer system 1100, various computer-readablemedia might be involved in providing instructions/code to processor(s)1110 for execution and/or might be used to store and/or carry suchinstructions/code. In many implementations, a computer-readable mediumis a physical and/or tangible storage medium. Such a medium may take theform of a non-volatile media or volatile media. Non-volatile mediainclude, for example, optical and/or magnetic disks, such as the storagedevice(s) 1125. Volatile media include, without limitation, dynamicmemory, such as the working memory 1135.

Common forms of physical and/or tangible computer-readable mediainclude, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, or any other magnetic medium, a CD-ROM, any other opticalmedium, punchcards, papertape, any other physical medium with patternsof holes, a RAM, a PROM, EPROM, a FLASH-EPROM, any other memory chip orcartridge, or any other medium from which a computer can readinstructions and/or code.

Various forms of computer-readable media may be involved in carrying oneor more sequences of one or more instructions to the processor(s) 1110for execution. Merely by way of example, the instructions may initiallybe carried on a magnetic disk and/or optical disc of a remote computer.A remote computer might load the instructions into its dynamic memoryand send the instructions as signals over a transmission medium to bereceived and/or executed by the computer system 1100.

The communications subsystem 1130 and/or components thereof generallywill receive signals, and the bus 1105 then might carry the signalsand/or the data, instructions, etc. carried by the signals to theworking memory 1135, from which the processor(s) 1110 retrieves andexecutes the instructions. The instructions received by the workingmemory 1135 may optionally be stored on a non-transitory storage device1125 either before or after execution by the processor(s) 1110.

The methods, systems, and devices discussed above are examples. Variousconfigurations may omit, substitute, or add various procedures orcomponents as appropriate. For instance, in alternative configurations,the methods may be performed in an order different from that described,and/or various stages may be added, omitted, and/or combined. Also,features described with respect to certain configurations may becombined in various other configurations. Different aspects and elementsof the configurations may be combined in a similar manner. Also,technology evolves and, thus, many of the elements are examples and donot limit the scope of the disclosure or claims.

Specific details are given in the description to provide a thoroughunderstanding of exemplary configurations including implementations.However, configurations may be practiced without these specific details.For example, well-known circuits, processes, algorithms, structures, andtechniques have been shown without unnecessary detail in order to avoidobscuring the configurations. This description provides exampleconfigurations only, and does not limit the scope, applicability, orconfigurations of the claims. Rather, the preceding description of theconfigurations will provide those skilled in the art with an enablingdescription for implementing described techniques. Various changes maybe made in the function and arrangement of elements without departingfrom the spirit or scope of the disclosure.

Also, configurations may be described as a process which is depicted asa schematic flowchart or block diagram. Although each may describe theoperations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations may be rearranged. A process may have additional steps notincluded in the figure. Furthermore, examples of the methods may beimplemented by hardware, software, firmware, middleware, microcode,hardware description languages, or any combination thereof. Whenimplemented in software, firmware, middleware, or microcode, the programcode or code segments to perform the necessary tasks may be stored in anon-transitory computer-readable medium such as a storage medium.Processors may perform the described tasks.

Having described several example configurations, various modifications,alternative constructions, and equivalents may be used without departingfrom the spirit of the disclosure. For example, the above elements maybe components of a larger system, wherein other rules may takeprecedence over or otherwise modify the application of the technology.Also, a number of steps may be undertaken before, during, or after theabove elements are considered. Accordingly, the above description doesnot bind the scope of the claims.

As used herein and in the appended claims, the singular forms “a”, “an”,and “the” include plural references unless the context clearly dictatesotherwise. Thus, for example, reference to “a user” includes a pluralityof such users, and reference to “the processor” includes reference toone or more processors and equivalents thereof known to those skilled inthe art, and so forth.

Also, the words “comprise”, “comprising”, “contains”, “containing”,“include”, “including”, and “includes”, when used in this specificationand in the following claims, are intended to specify the presence ofstated features, integers, components, or steps, but they do notpreclude the presence or addition of one or more other features,integers, components, steps, acts, or groups.

What is claimed is:
 1. A system comprising: one or more processors; anda non-transitory computer-readable medium comprising instructions that,when executed by the one or more processors, cause the one or moreprocessors to perform operations comprising: generate, using a firstfeature generator configured to receive a first type of genealogy itemsand to generate a feature vector for each of the received first type ofgenealogy items, a first set of feature vectors for each of a pluralityof the first type of genealogy items; generate, using a second featuregenerator configured to receive a second type of genealogy items and togenerate a feature vector for each of the received second type ofgenealogy items, a second set of feature vectors for each of a pluralityof the second type of genealogy items; generate, using a featureextender configured to transform a size of an input feature vector to anextended size, a set of extended feature vectors comprising featurevectors from the first and second sets of feature vectors, wherein eachof the extended feature vectors has a normalized vector size; input theset of extended feature vectors to a machine learning model configuredto generate a rank-ordered list of the set of extended feature vectors;and store a portion of the genealogy items corresponding to the extendedfeature vectors in rank-ordered list.
 2. The system of claim 1, whereinthe genealogy items are retrieved from a plurality of pedigrees storedon a genealogy database, the plurality of pedigrees corresponding to asame person.
 3. The system of claim 2, wherein the plurality ofpedigrees stored on the genealogy database are identified ascorresponding to the same person using an entity-resolution machinelearning model.
 4. The system of claim 1, further comprising theoperations: generate, using a third feature generator configured toreceive a third type of genealogy items and to generate a feature vectorfor each of the received third type of genealogy items, a third set offeature vectors for each of a plurality of the third type of genealogyitems; wherein the first, second, and third types of genealogy itemscomprise a record type, an image type, and a story type, respectively.5. The system of claim 4, wherein a feature value of a feature vectorfor a story type of genealogy items is based on a similarity valuebetween story details and tree person details.
 6. The system of claim 1,wherein the feature vectors comprise record-specific features andrelevance features.
 7. The system of claim 1, wherein feature values ofthe first and second sets of feature vectors for the first and secondtypes of genealogy items are normalized.
 8. A non-transitorycomputer-readable medium comprising instructions that, when executed byone or more processors, cause the one or more processors to performoperations comprising: generate, using a first feature generatorconfigured to receive a first type of genealogy items and to generate afeature vector for each of the received first type of genealogy items, afirst set of feature vectors for each of a plurality of the first typeof genealogy items; generate, using a second feature generatorconfigured to receive a second type of genealogy items and to generate afeature vector for each of the received second type of genealogy items,a second set of feature vectors for each of a plurality of the secondtype of genealogy items; generate, using a feature extender configuredto transform a size of an input feature to an extended size, a set ofextended feature vectors comprising feature vectors from the first andsecond sets of feature vectors, wherein each of the extended featurevectors has a normalized vector size; input the set of extended featurevectors to a machine learning model configured to generate arank-ordered list of the set of extended feature vectors; and store aportion of the genealogy items corresponding to the extended featurevectors in rank-ordered list.
 9. The non-transitory computer-readablemedium of claim 8, wherein the genealogy items are retrieved from aplurality of pedigrees stored on a genealogy database, the plurality ofpedigrees corresponding to a same person.
 10. The non-transitorycomputer-readable medium of claim 9, wherein the plurality of pedigreesstored on the genealogy database are identified as corresponding to thesame person using an entity-resolution machine learning model utilizinga two-step learning process.
 11. The non-transitory computer-readablemedium of claim 8, further comprising the operations: generate, using athird feature generator configured to receive a third type of genealogyitems and to generate a feature vector for each of the received thirdtype of genealogy items, a third set of feature vectors for each of aplurality of the third type of genealogy items; wherein the first,second, and third types of genealogy items comprise a record type, animage type, and a story type, respectively.
 12. The non-transitorycomputer-readable medium of claim 11, wherein a feature value of afeature vector for a story type of genealogy items is based on asimilarity value between story details and tree person details.
 13. Thenon-transitory computer-readable medium of claim 8, wherein the featurevectors comprise record-specific features and relevance features. 14.The non-transitory computer-readable medium of claim 8, wherein featurevalues of the first and second sets of feature vectors for the first andsecond types of genealogy items are normalized.
 15. A method forrank-listing genealogical items of different genealogical-item types,the method comprising: generating, using a first feature generatorconfigured to receive a first type of genealogy items and to generate afeature vector for each of the received first type of genealogy items, afirst set of feature vectors for each of a plurality of the first typeof genealogy items; generating, using a second feature generatorconfigured to receive a second type of genealogy items and to generate afeature vector for each of the received second type of genealogy items,a second set of feature vectors for each of a plurality of the secondtype of genealogy items; generating, using a feature extender configuredto transform a size of an input feature to an extended size, a set ofextended feature vectors comprising feature vectors from the first andsecond sets of feature vectors, wherein each of the extended featurevectors has a normalized vector size; inputting the set of extendedfeature vectors to a machine learning model configured to generate arank-ordered list of the set of extended feature vectors; and storing aportion of the genealogy items corresponding to the extended featurevectors in rank-ordered list.
 16. The method of claim 15, wherein thegenealogy items are retrieved from a plurality of pedigrees stored on agenealogy database, the plurality of pedigrees corresponding to a sameperson.
 17. The method of claim 16, wherein the plurality of pedigreesstored on the genealogy database are identified as corresponding to thesame person using a hierarchical classification entity-resolutionmachine learning model.
 18. The method of claim 15, further comprising:generating, using a third feature generator configured to receive athird type of genealogy items and to generate a feature vector for eachof the received third type of genealogy items, a third set of featurevectors for each of a plurality of the third type of genealogy items;wherein the first, second, and third types of genealogy items comprise arecord type, an image type, and a story type, respectively.
 19. Themethod of claim 18, wherein a feature value of a feature vector for astory type of genealogy items is based on a similarity value betweenstory details and tree person details.
 20. The method of claim 15,wherein feature values of the first and second sets of feature vectorsfor the first and second types of genealogy items are normalized.